In the fields of medicine and biology, there is a wide range of diagnostic techniques based on the analysis of visual characteristics. In recent years the trend has been to automate the diagnostic techniques. In order to successfully automate such techniques, computational tools must be available to efficiently quantify the visual attributes of objects found in the digitized images or the biological specimens.
One example of such an analysis technique is the quantitative analysis of visual texture. There exist several automated or semi-automated diagnostic instruments for the pre-screening of gynaecological smears. The object of these pre-screening instruments is to attempt an identification of cervical epithelial cells which exhibit cancerous or pre-cancerous attributes. The pre-screening procedure involves assessing the state of genetic activity of any cell and more particularly the distribution of the genetic material within its nucleus. The physical distribution of the genetic material in the nucleus is commonly known as chromatin (FIG. 1), and manifests itself as a visual texture in a digitized micrograph. Thus, the ability to accurately analyze the nuclear texture is a vital step in the rendering of an automated diagnostic decision.
The calculation of a texture feature, i.e. nuclear texture, generally relies on an analysis of the distribution of the digitized pixel intensities within the digital image. In the art, there are four principal approaches to optical texture analysis that yield quantitative parameters. The first approach is known as "Markovian" analysis. According to the Markovian analysis technique, the digitized image is treated as a form of stochastic process carrying the image into a transition-probability space. The second approach comprises "gradient analysis" where the grey-scale image is treated as a scalar and differentiable function with texture parameters based on the direction and rate of spatial variations. The third approach comprises a "granulometric" analysis. The granulometric technique analyzes textures by characterizing blocks of the image according to size, shape, grey-level content and distribution. The fourth commonly used approach comprises "orthogonal transformation" analysis. The orthogonal transformation analysis technique essentially involves a mapping of the image into some sort of orthogonal space, for example a Fourier space, where the expansion coefficients can be used to characterize the image texture.
While the commonly known texture feature analysis techniques briefly described above are suitable for analyzing nuclear texture, implementation of the techniques into an automated instrument presents significant obstacles. The primary obstacle is the requirement for substantial computational power due to the calculation-intensive nature of the analysis techniques.
What is required is a method which integrates the aspects of these analysis techniques into a process which can be implemented and executed in an automated diagnostic decision making instrument.